Background noise or ambient noise is any sound other than the sound being monitored. Typically, background noise may be caused by engines, blowers, fans, air conditioners, cars, busy intersections, people talking in restaurants etc. If untreated, background noise can be annoying at times. Further, stochastic background noise is a major problem when processing wideband audio signals.
Modern day communication devices operate in a myriad type of environments. Examples of communication devices include, but are not limited to, a mobile phone, wireless telephone and so forth. These communication devices may be narrowband or wideband depending on the sampling frequency in which they operate. Narrowband and wideband communication devices operate at 8000 Hz and 16000 Hz sampling frequency respectively. Some of the environments may be extremely noisy, for example bars, crowded restaurants and so forth, while some environments may be extremely quite such as home, relaxing lounge and so forth. Generally, the communication devices include microphone(s) that pick up the desired signal of the user and background noise (if present). As a result, the communication at the other end may not be clearly discernable or pleasant.
Typically for noise reduction in wideband audio signals, the audio signal is processed in a microprocessor by using noise reduction algorithms. The audio signal is picked up by the microphone, digitized by an Analog to Digital Converter (ADC) and fed to the microprocessor for analysis and noise reduction. Attempts to solve this problem have largely been unsuccessful. Traditional noise reduction systems use filters, Wiener filters and Kalman filters. However, these techniques have largely been ineffective mostly due to the fact that the systems need to have prior knowledge of the properties of noise. Other noise reduction systems use spectral subtraction. One such technique is described by Steven F. Boll in “Suppression of Acoustic Noise in Speech Using Spectral Subtraction”, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. Assp-27, No. 2, April 1979 and is incorporated herein by reference. Spectral subtraction involves calculating the Fast Fourier Transform (FFT) of the audio signal, estimating the magnitude spectrum of the signal classified as “noisy speech” and subtracting the magnitude spectrum of the signal classified as “noise only”, and then calculating the Inverse FFT (IFFT) of the wideband audio signal. However, the spectral subtraction technique introduces a lot of artifacts and “musical noise”. It requires a continuous averaging of the noise. The technique performs well for stationary noise, where the characteristics of the noise do not vary over time. However, they do not perform well for stochastic noises which are non-deterministic, time varying and non-stationary. Examples of stochastic noises include restaurant noise, keyboard clicks, wind noise, cafeteria noise and so forth.
Single microphone and two microphone noise reduction systems have been attempted. For example, U.S. Pat. No. 6,415,034 to Hietanen et al patent describes the use of a second background noise microphone located within an earphone unit or behind an ear capsule. Digital signal processing is used to create a noise canceling signal which enters the speech microphone. Unfortunately, the effectiveness of the method disclosed in the Hietanen et al patent is compromised by acoustical leakage that is where the ambient or environmental noise leaks past the ear capsule and into the speech microphone. The Hietanen et al patent also relies upon complex and power consuming expensive digital circuitry that may generally not be suitable for small portable battery powered devices such as pocket able cellular telephones, Bluetooth headsets, cordless phones and so forth.
Another example is U.S. Pat. No. 5,969,838 (the “Paritsky patent”) which discloses a noise reduction system utilizing two fiber optic microphones that are placed side-by-side next to one another. Unfortunately, the Paritsky patent discloses a system using light guides and other relatively expensive and/or fragile components not suitable for the rigors of cellular telephones, Bluetooth headsets, cordless phones and so forth.
U.S. Pat. No. 5,406,622 to Silverberg et al uses two adaptive filters, one driven by the handset transmitter to subtract speech from a reference value to produce an enhanced reference signal; and a second adaptive filter driven by the enhanced reference signal to subtract noise from the transmitter. Silverberg et al require accurate detection of speech and non-speech regions. Any incorrect detection will degrade the performance of the system.
In light of the above discussion, techniques are required to reduce the stochastic noise in a wideband audio signal.